title: RESOLVING CLUSTERED WORMS VIA PROBABILISTIC SHAPE MODELS.
abstract: The roundworm Caenorhabditis elegans is an effective model system for biological processes such as immunity, behavior, and metabolism. Robotic sample preparation together with automated microscopy and image analysis has recently enabled high-throughput screening experiments using C. elegans. So far, such experiments have been limited to per-image measurements due to the tendency of the worms to cluster, which prevents extracting features from individual animals.We present a novel approach for the extraction of individual C. elegans from clusters of worms in high-throughput microscopy images. The key ideas are the construction of a low-dimensional shape-descriptor space and the definition of a probability measure on it. Promising segmentation results are shown.
authors.affiliation: Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA.
authors.forename: Carolina
authors.initials: C
authors.lastname: Wählby
authors.forename: Tammy
authors.initials: T
authors.lastname: Riklin-Raviv
authors.forename: Vebjorn
authors.initials: V
authors.lastname: Ljosa
authors.forename: Annie L
authors.initials: AL
authors.lastname: Conery
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
authors.forename: Frederick M
authors.initials: FM
authors.lastname: Ausubel
authors.forename: Anne E
authors.initials: AE
authors.lastname: Carpenter